Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques for Building Intelligent Systems

Géron, Aurélien

ISBN 10: 1491962291 / ISBN 13: 9781491962299

Published by O'Reilly Media, 2017

About this Item

Befriedigend/Good: Durchschnittlich erhaltenes Buch bzw. Schutzumschlag mit Gebrauchsspuren, aber vollständigen Seiten. / Describes the average WORN book or dust jacket that has all the pages present.

Seller Inventory # M01491962291-G

Bibliographic Details

• Title: Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques for Building Intelligent Systems
• Publisher: O'Reilly Media
• Publication Date: 2017
• Language: English
• ISBN 10: 1491962291
• ISBN 13: 9781491962299
• Binding: Soft cover
• Condition: good

About this title

Synopsis

Graphics in this book are printed in black and white.

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.

By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.

• Explore the machine learning landscape, particularly neural nets
• Use scikit-learn to track an example machine-learning project end-to-end
• Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
• Use the TensorFlow library to build and train neural nets
• Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
• Learn techniques for training and scaling deep neural nets
• Apply practical code examples without acquiring excessive machine learning theory or algorithm details

About the Author

Aurelien Geron has worked as a software engineer for a consulting firm in Paris, an IoT startup in Montreal (back in 1999!), and has also worked as co-founder and CTO of a leading wireless ISP in France (Wifirst). He was the product manager for YouTube's video classification team.He has authored a WiFi book, a C++ book, and taught CS in French engineering schools. A few personal fun facts: Aurelien grew up in France, Nigeria, New Zealand, and the U.S. (Berkeley). He studied microbiology and evolutionary genetics before going into software engineering. He was the singer in a rock band, has 2 turtles and 3 hens, has scuba dived with 10-foot sharks, taught his 5-year-old son to count in binary on his fingers (up to 1023), and his parachute didn't open on the 2nd jump.